Histopathology Feature Mining and Association with Hyperspectral Imaging for the Detection of Squamous Neoplasia

• Type of Material: Article
• Authors: Guolan Lu, Emory University; Dongsheng Wang, Emory University; Xulei Qin, Emory University; Susan Muller, Emory University; James Little III, Emory University; Xu Wang, Emory University; Amy Chen, Emory University; Georgia Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2019-11-28
• Publisher: Nature Publishing Group
• Publication Version: Final Published Version
• Copyright Statement: © 2019, The Author(s)
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-019-54139-5
• Title of Journal or Parent Work: Scientific Reports
• Volume: 9
• Issue: 1
• Start Page: 17863
• End Page: 17863
• Grant/Funding Information: Early Translational Research Award (RP190588) from the Cancer Prevention and Research Institute of Texas (CPRIT); NIH grants (R01CA156775, R01CA204254, and R01HL140325)
• Keywords: Multidisciplinary Sciences; REFLECTANCE; INVERSE MODEL; Science & Technology; Science & Technology - Other Topics; DIAGNOSIS; LIGHT; TISSUE OPTICAL-PROPERTIES; CLASSIFICATION
• Research Categories: Health Sciences, Pathology; Engineering, Biomedical; Health Sciences, Oncology

Abstract

Hyperspectral imaging (HSI) is a noninvasive optical modality that holds promise for early detection of tongue lesions. Spectral signatures generated by HSI contain important diagnostic information that can be used to predict the disease status of the examined biological tissue. However, the underlying pathophysiology for the spectral difference between normal and neoplastic tissue is not well understood. Here, we propose to leverage digital pathology and predictive modeling to select the most discriminative features from digitized histological images to differentiate tongue neoplasia from normal tissue, and then correlate these discriminative pathological features with corresponding spectral signatures of the neoplasia. We demonstrated the association between the histological features quantifying the architectural features of neoplasia on a microscopic scale, with the spectral signature of the corresponding tissue measured by HSI on a macroscopic level. This study may provide insight into the pathophysiology underlying the hyperspectral dataset.